NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM
Authors: Hamed Akbari, Spyridon Bakas, Chiharu Sako, Anahita Fathi Kazerooni, Javier Villanueva-Meyer, Jose Garcia, Stephen Bagley, Ujjwal Baid, Michel Bilello, Steven Brem, Robert Lustig, Suyash Mohan, MacLean Nasrallah, Donald O'Rourke, Evan Calabrese, Jeffrey Rudie, Pamela LaMontagne, Daniel Marcus, Carmen Balana, Jaume Capellades, Josep Puig, Jill Barnholtz-Sloan, Chaitra Badve, Andrew Sloan, Murat Ak, Rivka Colen, Sung Soo Ahn, Jong Hee Chang, Yoon Seong Choi, Seung-Koo Lee, Adam Dicker, Adam Flanders, Wenyin Shi, Gaurav Shukla, Brent Griffith, Laila Poisson, Lisa Rogers, Thomas Booth, Rajan Jain, Matthew Lee, Abhishek Mahajan, Arnab Chakravarti, Joshua Palmer, William Taylor, Santiago Cepeda, Benedikt Wiestler, Christos Davatzikos
Journal: Neuro-Oncology (2022)

Abstract PURPOSE Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizable prognostic stratification. We seek an AI-based OS predictor using routine clinically acquired MRI sequences, quantitatively evaluated across institutions of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS We identified a retrospective cohort of 2,293 diffuse glioma (IDH-wildtype/-NOS/-NEC) patients from 22 geographically distinct institutions across 3 continents, with preoperative structural MRI scans. The entire tumor burden was automatically segmented into 3 sub-compartments, i.e., enhancing, necrotic, peritumoral T2-FLAIR abnormality. We developed our AI predictor by multivariate integration of i)patient age, ii)tumor sub-compartment volume normalized to brain volume, iii)spatial distribution characteristics (tumor location, distance to the ventricles, and laterality), and iv)morphologic descriptors (major axes’ length, axes’ ratio, extent, and number of tumors). The AI predictor returns a continuous value between 0-1, defining short-, intermediate-, and long-survivors based on thresholds on the 25th and 75th percentiles. Leave-One-Site-Out-Cross-Validation was used to assess the generalizability of our stratification. Kaplan-Meier survival curves were computed for OS analysis and evaluated by a Cox proportional hazards model for statistical significance and hazard ratios. RESULTS Survival analysis yielded a hazard ratio of 2.07 (95%CI, 2.06-2.08, p-value= 4.8e-102) for patient stratification into short-, intermediate-, and long-survivors. Pearson correlation between the predicted and actual OS yielded an R= 0.49. CONCLUSION Multivariate integration of visually quantified tumor characteristics, agnostic to acquisition protocol/equipment, yields an accurate OS surrogate index. Validation of our AI model in the largest centralized glioblastoma imaging dataset, from the ReSPOND consortium, supports its generalizability across diverse patient populations and acquisition settings, potentially contributing to equitable improvements of personalized patient care.